Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Estimation of the population mean or population total is considered. A new calibration estimator with a new likelihood function is proposed. The resultant estimator derives from the two-step calibration by Singh and Sedory (2013 Singh, S., and S. A. Sedory. 2013. Two-step calibration of design weights in survey sampling. In JSM Proceedings, Survey Research Methods Section, 2928–2942, Montreal, Canada. [Google Scholar], 2016 Singh, S., and S. A. Sedory. 2016. Two-step calibration of design weights in survey sampling. Communications in Statistics: Theory and Methods 45 (12):3510. doi:10.1080/03610926.2014.892137.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). The resultant estimator takes the form of Searls’ (1964) estimator. The Berger and De La Riva Torres (2016 Berger, Y. G., and O. De La Riva Torres. 2016. Empirical likelihood confidence intervals for complex sampling designs. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 78 (2):319–41.[Crossref], [Web of Science ®] , [Google Scholar]) methodology is shown to be a special case of the proposed power method of calibration. Conditional estimators of the mean squared errors of the empirical likelihood estimator and the proposed estimators are developed and investigated through extensive simulation study following Breidt and Opsomer (2000 Breidt, F. J., and J. D. Opsomer. 2000. Local polynomial regression estimators in survey sampling. The Annals of Statistics 28 (4):1026–53.[Crossref], [Web of Science ®] , [Google Scholar]) and Breidt et al. (2016 Breidt, F. J., J. D. Opsomer, and I. Sanchez-Borrego. 2016. Nonparametric variance estimation under fine stratification: An alternative to collapsed strata. Journal of the American Statistical Association 111 (514):822–33.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it